27 citations · 78 across the 13 of their papers we have counts for
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stat.ME2023★ 1 cited
Top-Two Thompson Sampling for Contextual Top-mc Selection Problems
Xinbo Shi, Yijie Peng, Gongbo Zhang
We aim to efficiently allocate a fixed simulation budget to identify the top-mc designs for each context among a finite number of contexts. The performance of each design under a c…
stat.ML2023★ 1 cited
Efficient Learning for Selecting Top-m Context-Dependent Designs
Gongbo Zhang, Sihua Chen, Kuihua Huang +1
We consider a simulation optimization problem for a context-dependent decision-making, which aims to determine the top-m designs for all contexts. Under a Bayesian framework, we fo…
cs.LG2023★ 1 cited
Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient Algorithms
Jinyang Jiang, Jiaqiao Hu, Yijie Peng
Classical reinforcement learning (RL) aims to optimize the expected cumulative reward. In this work, we consider the RL setting where the goal is to optimize the quantile of the cu…